{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Autonomous driving - Car detection\n",
    "\n",
    "Welcome to your week 3 programming assignment. You will learn about object detection using the very powerful YOLO model. Many of the ideas in this notebook are described in the two YOLO papers: Redmon et al., 2016 (https://arxiv.org/abs/1506.02640) and Redmon and Farhadi, 2016 (https://arxiv.org/abs/1612.08242). \n",
    "\n",
    "**You will learn to**:\n",
    "- Use object detection on a car detection dataset\n",
    "- Deal with bounding boxes\n",
    "\n",
    "Run the following cell to load the packages and dependencies that are going to be useful for your journey!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\program_files\\anaconda\\lib\\site-packages\\h5py\\__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
      "  from ._conv import register_converters as _register_converters\n",
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import argparse\n",
    "import os\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.pyplot import imshow\n",
    "import scipy.io\n",
    "import scipy.misc\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import PIL\n",
    "import tensorflow as tf\n",
    "from keras import backend as K\n",
    "from keras.layers import Input, Lambda, Conv2D\n",
    "from keras.models import load_model, Model\n",
    "from yolo_utils import read_classes, read_anchors, generate_colors, preprocess_image, draw_boxes, scale_boxes\n",
    "from yad2k.models.keras_yolo import yolo_head, yolo_boxes_to_corners, preprocess_true_boxes, yolo_loss, yolo_body\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Important Note**: As you can see, we import Keras's backend as K. This means that to use a Keras function in this notebook, you will need to write: `K.function(...)`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1 - Problem Statement\n",
    "\n",
    "You are working on a self-driving car. As a critical component of this project, you'd like to first build a car detection system. To collect data, you've mounted a camera to the hood (meaning the front) of the car, which takes pictures of the road ahead every few seconds while you drive around. \n",
    "\n",
    "<center>\n",
    "<video width=\"400\" height=\"200\" src=\"nb_images/road_video_compressed2.mp4\" type=\"video/mp4\" controls>\n",
    "</video>\n",
    "</center>\n",
    "\n",
    "<caption><center> Pictures taken from a car-mounted camera while driving around Silicon Valley. <br> We would like to especially thank [drive.ai](https://www.drive.ai/) for providing this dataset! Drive.ai is a company building the brains of self-driving vehicles.\n",
    "</center></caption>\n",
    "\n",
    "<img src=\"nb_images/driveai.png\" style=\"width:100px;height:100;\">\n",
    "\n",
    "You've gathered all these images into a folder and have labelled them by drawing bounding boxes around every car you found. Here's an example of what your bounding boxes look like.\n",
    "\n",
    "<img src=\"nb_images/box_label.png\" style=\"width:500px;height:250;\">\n",
    "<caption><center> <u> **Figure 1** </u>: **Definition of a box**<br> </center></caption>\n",
    "\n",
    "If you have 80 classes that you want YOLO to recognize, you can represent the class label $c$ either as an integer from 1 to 80, or as an 80-dimensional vector (with 80 numbers) one component of which is 1 and the rest of which are 0. The video lectures had used the latter representation; in this notebook, we will use both representations, depending on which is more convenient for a particular step.  \n",
    "\n",
    "In this exercise, you will learn how YOLO works, then apply it to car detection. Because the YOLO model is very computationally expensive to train, we will load pre-trained weights for you to use. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2 - YOLO"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "YOLO (\"you only look once\") is a popular algoritm because it achieves high accuracy while also being able to run in real-time. This algorithm \"only looks once\" at the image in the sense that it requires only one forward propagation pass through the network to make predictions. After non-max suppression, it then outputs recognized objects together with the bounding boxes.\n",
    "\n",
    "### 2.1 - Model details\n",
    "\n",
    "First things to know:\n",
    "- The **input** is a batch of images of shape (m, 608, 608, 3)\n",
    "- The **output** is a list of bounding boxes along with the recognized classes. Each bounding box is represented by 6 numbers $(p_c, b_x, b_y, b_h, b_w, c)$ as explained above. If you expand $c$ into an 80-dimensional vector, each bounding box is then represented by 85 numbers. \n",
    "\n",
    "We will use 5 anchor boxes. So you can think of the YOLO architecture as the following: IMAGE (m, 608, 608, 3) -> DEEP CNN -> ENCODING (m, 19, 19, 5, 85).\n",
    "\n",
    "Lets look in greater detail at what this encoding represents. \n",
    "\n",
    "<img src=\"nb_images/architecture.png\" style=\"width:700px;height:400;\">\n",
    "<caption><center> <u> **Figure 2** </u>: **Encoding architecture for YOLO**<br> </center></caption>\n",
    "\n",
    "If the center/midpoint of an object falls into a grid cell, that grid cell is responsible for detecting that object."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since we are using 5 anchor boxes, each of the 19 x19 cells thus encodes information about 5 boxes. Anchor boxes are defined only by their width and height.\n",
    "\n",
    "For simplicity, we will flatten the last two last dimensions of the shape (19, 19, 5, 85) encoding. So the output of the Deep CNN is (19, 19, 425).\n",
    "\n",
    "<img src=\"nb_images/flatten.png\" style=\"width:700px;height:400;\">\n",
    "<caption><center> <u> **Figure 3** </u>: **Flattening the last two last dimensions**<br> </center></caption>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, for each box (of each cell) we will compute the following elementwise product and extract a probability that the box contains a certain class.\n",
    "\n",
    "<img src=\"nb_images/probability_extraction.png\" style=\"width:700px;height:400;\">\n",
    "<caption><center> <u> **Figure 4** </u>: **Find the class detected by each box**<br> </center></caption>\n",
    "\n",
    "Here's one way to visualize what YOLO is predicting on an image:\n",
    "- For each of the 19x19 grid cells, find the maximum of the probability scores (taking a max across both the 5 anchor boxes and across different classes). \n",
    "- Color that grid cell according to what object that grid cell considers the most likely.\n",
    "\n",
    "Doing this results in this picture: \n",
    "\n",
    "<img src=\"nb_images/proba_map.png\" style=\"width:300px;height:300;\">\n",
    "<caption><center> <u> **Figure 5** </u>: Each of the 19x19 grid cells colored according to which class has the largest predicted probability in that cell.<br> </center></caption>\n",
    "\n",
    "Note that this visualization isn't a core part of the YOLO algorithm itself for making predictions; it's just a nice way of visualizing an intermediate result of the algorithm. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Another way to visualize YOLO's output is to plot the bounding boxes that it outputs. Doing that results in a visualization like this:  \n",
    "\n",
    "<img src=\"nb_images/anchor_map.png\" style=\"width:200px;height:200;\">\n",
    "<caption><center> <u> **Figure 6** </u>: Each cell gives you 5 boxes. In total, the model predicts: 19x19x5 = 1805 boxes just by looking once at the image (one forward pass through the network)! Different colors denote different classes. <br> </center></caption>\n",
    "\n",
    "In the figure above, we plotted only boxes that the model had assigned a high probability to, but this is still too many boxes. You'd like to filter the algorithm's output down to a much smaller number of detected objects. To do so, you'll use non-max suppression. Specifically, you'll carry out these steps: \n",
    "- Get rid of boxes with a low score (meaning, the box is not very confident about detecting a class)\n",
    "- Select only one box when several boxes overlap with each other and detect the same object.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.2 - Filtering with a threshold on class scores\n",
    "\n",
    "You are going to apply a first filter by thresholding. You would like to get rid of any box for which the class \"score\" is less than a chosen threshold. \n",
    "\n",
    "The model gives you a total of 19x19x5x85 numbers, with each box described by 85 numbers. It'll be convenient to rearrange the (19,19,5,85) (or (19,19,425)) dimensional tensor into the following variables:  \n",
    "- `box_confidence`: tensor of shape $(19 \\times 19, 5, 1)$ containing $p_c$ (confidence probability that there's some object) for each of the 5 boxes predicted in each of the 19x19 cells.\n",
    "- `boxes`: tensor of shape $(19 \\times 19, 5, 4)$ containing $(b_x, b_y, b_h, b_w)$ for each of the 5 boxes per cell.\n",
    "- `box_class_probs`: tensor of shape $(19 \\times 19, 5, 80)$ containing the detection probabilities $(c_1, c_2, ... c_{80})$ for each of the 80 classes for each of the 5 boxes per cell.\n",
    "\n",
    "**Exercise**: Implement `yolo_filter_boxes()`.\n",
    "1. Compute box scores by doing the elementwise product as described in Figure 4. The following code may help you choose the right operator: \n",
    "```python\n",
    "a = np.random.randn(19*19, 5, 1)\n",
    "b = np.random.randn(19*19, 5, 80)\n",
    "c = a * b # shape of c will be (19*19, 5, 80)\n",
    "```\n",
    "2. For each box, find:\n",
    "    - the index of the class with the maximum box score ([Hint](https://keras.io/backend/#argmax)) (Be careful with what axis you choose; consider using axis=-1)\n",
    "    - the corresponding box score ([Hint](https://keras.io/backend/#max)) (Be careful with what axis you choose; consider using axis=-1)\n",
    "3. Create a mask by using a threshold. As a reminder: `([0.9, 0.3, 0.4, 0.5, 0.1] < 0.4)` returns: `[False, True, False, False, True]`. The mask should be True for the boxes you want to keep. \n",
    "4. Use TensorFlow to apply the mask to box_class_scores, boxes and box_classes to filter out the boxes we don't want. You should be left with just the subset of boxes you want to keep. ([Hint](https://www.tensorflow.org/api_docs/python/tf/boolean_mask))\n",
    "\n",
    "Reminder: to call a Keras function, you should use `K.function(...)`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# GRADED FUNCTION: yolo_filter_boxes\n",
    "\n",
    "def yolo_filter_boxes(box_confidence, boxes, box_class_probs, threshold = .6):\n",
    "    \"\"\"Filters YOLO boxes by thresholding on object and class confidence.\n",
    "    \n",
    "    Arguments:\n",
    "    box_confidence -- tensor of shape (19, 19, 5, 1)\n",
    "    boxes -- tensor of shape (19, 19, 5, 4)\n",
    "    box_class_probs -- tensor of shape (19, 19, 5, 80)\n",
    "    threshold -- real value, if [ highest class probability score < threshold], then get rid of the corresponding box\n",
    "    \n",
    "    Returns:\n",
    "    scores -- tensor of shape (None,), containing the class probability score for selected boxes\n",
    "    boxes -- tensor of shape (None, 4), containing (b_x, b_y, b_h, b_w) coordinates of selected boxes\n",
    "    classes -- tensor of shape (None,), containing the index of the class detected by the selected boxes\n",
    "    \n",
    "    Note: \"None\" is here because you don't know the exact number of selected boxes, as it depends on the threshold. \n",
    "    For example, the actual output size of scores would be (10,) if there are 10 boxes.\n",
    "    \"\"\"\n",
    "    \n",
    "    # Step 1: Compute box scores\n",
    "    ### START CODE HERE ### (≈ 1 line)\n",
    "    box_scores = box_confidence * box_class_probs\n",
    "    ### END CODE HERE ###\n",
    "    \n",
    "    # Step 2: Find the box_classes thanks to the max box_scores, keep track of the corresponding score\n",
    "    ### START CODE HERE ### (≈ 2 lines)\n",
    "    box_classes = K.argmax(box_scores, axis=-1) #最大值下标\n",
    "    box_class_scores = K.max(box_scores, axis=-1)\n",
    "    ### END CODE HERE ###\n",
    "    \n",
    "    # Step 3: Create a filtering mask based on \"box_class_scores\" by using \"threshold\". The mask should have the\n",
    "    # same dimension as box_class_scores, and be True for the boxes you want to keep (with probability >= threshold)\n",
    "    ### START CODE HERE ### (≈ 1 line)\n",
    "    filtering_mask = (box_class_scores >= threshold)\n",
    "    ### END CODE HERE ###\n",
    "    \n",
    "    # Step 4: Apply the mask to scores, boxes and classes\n",
    "    ### START CODE HERE ### (≈ 3 lines)\n",
    "    scores = tf.boolean_mask(box_class_scores, filtering_mask)\n",
    "    boxes = tf.boolean_mask(boxes, filtering_mask)\n",
    "    classes = tf.boolean_mask(box_classes, filtering_mask)\n",
    "    ### END CODE HERE ###\n",
    "    \n",
    "    return scores, boxes, classes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "scores[2] = 10.750582\n",
      "boxes[2] = [ 8.426533   3.2713668 -0.5313436 -4.9413733]\n",
      "classes[2] = 7\n",
      "scores.shape = (?,)\n",
      "boxes.shape = (?, 4)\n",
      "classes.shape = (?,)\n"
     ]
    }
   ],
   "source": [
    "with tf.Session() as test_a:\n",
    "    box_confidence = tf.random_normal([19, 19, 5, 1], mean=1, stddev=4, seed = 1)\n",
    "    boxes = tf.random_normal([19, 19, 5, 4], mean=1, stddev=4, seed = 1)\n",
    "    box_class_probs = tf.random_normal([19, 19, 5, 80], mean=1, stddev=4, seed = 1)\n",
    "    scores, boxes, classes = yolo_filter_boxes(box_confidence, boxes, box_class_probs, threshold = 0.5)\n",
    "    print(\"scores[2] = \" + str(scores[2].eval()))\n",
    "    print(\"boxes[2] = \" + str(boxes[2].eval()))\n",
    "    print(\"classes[2] = \" + str(classes[2].eval()))\n",
    "    print(\"scores.shape = \" + str(scores.shape))\n",
    "    print(\"boxes.shape = \" + str(boxes.shape))\n",
    "    print(\"classes.shape = \" + str(classes.shape))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Expected Output**:\n",
    "\n",
    "<table>\n",
    "    <tr>\n",
    "        <td>\n",
    "            **scores[2]**\n",
    "        </td>\n",
    "        <td>\n",
    "           10.7506\n",
    "        </td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "        <td>\n",
    "            **boxes[2]**\n",
    "        </td>\n",
    "        <td>\n",
    "           [ 8.42653275  3.27136683 -0.5313437  -4.94137383]\n",
    "        </td>\n",
    "    </tr>\n",
    "\n",
    "    <tr>\n",
    "        <td>\n",
    "            **classes[2]**\n",
    "        </td>\n",
    "        <td>\n",
    "           7\n",
    "        </td>\n",
    "    </tr>\n",
    "        <tr>\n",
    "        <td>\n",
    "            **scores.shape**\n",
    "        </td>\n",
    "        <td>\n",
    "           (?,)\n",
    "        </td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "        <td>\n",
    "            **boxes.shape**\n",
    "        </td>\n",
    "        <td>\n",
    "           (?, 4)\n",
    "        </td>\n",
    "    </tr>\n",
    "\n",
    "    <tr>\n",
    "        <td>\n",
    "            **classes.shape**\n",
    "        </td>\n",
    "        <td>\n",
    "           (?,)\n",
    "        </td>\n",
    "    </tr>\n",
    "\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.3 - Non-max suppression ###\n",
    "\n",
    "Even after filtering by thresholding over the classes scores, you still end up a lot of overlapping boxes. A second filter for selecting the right boxes is called non-maximum suppression (NMS). "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "<img src=\"nb_images/non-max-suppression.png\" style=\"width:500px;height:400;\">\n",
    "<caption><center> <u> **Figure 7** </u>: In this example, the model has predicted 3 cars, but it's actually 3 predictions of the same car. Running non-max suppression (NMS) will select only the most accurate (highest probabiliy) one of the 3 boxes. <br> </center></caption>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Non-max suppression uses the very important function called **\"Intersection over Union\"**, or IoU.\n",
    "<img src=\"nb_images/iou.png\" style=\"width:500px;height:400;\">\n",
    "<caption><center> <u> **Figure 8** </u>: Definition of \"Intersection over Union\". <br> </center></caption>\n",
    "\n",
    "**Exercise**: Implement iou(). Some hints:\n",
    "- In this exercise only, we define a box using its two corners (upper left and lower right): (x1, y1, x2, y2) rather than the midpoint and height/width.\n",
    "- To calculate the area of a rectangle you need to multiply its height (y2 - y1) by its width (x2 - x1)\n",
    "- You'll also need to find the coordinates (xi1, yi1, xi2, yi2) of the intersection of two boxes. Remember that:\n",
    "    - xi1 = maximum of the x1 coordinates of the two boxes\n",
    "    - yi1 = maximum of the y1 coordinates of the two boxes\n",
    "    - xi2 = minimum of the x2 coordinates of the two boxes\n",
    "    - yi2 = minimum of the y2 coordinates of the two boxes\n",
    "    \n",
    "In this code, we use the convention that (0,0) is the top-left corner of an image, (1,0) is the upper-right corner, and (1,1) the lower-right corner. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# GRADED FUNCTION: iou\n",
    "\n",
    "def iou(box1, box2):\n",
    "    \"\"\"Implement the intersection over union (IoU) between box1 and box2\n",
    "    \n",
    "    Arguments:\n",
    "    box1 -- first box, list object with coordinates (x1, y1, x2, y2)\n",
    "    box2 -- second box, list object with coordinates (x1, y1, x2, y2)\n",
    "    \"\"\"\n",
    "\n",
    "    # Calculate the (y1, x1, y2, x2) coordinates of the intersection of box1 and box2. Calculate its Area.\n",
    "    ### START CODE HERE ### (≈ 5 lines)\n",
    "    xi1 = max(box1[0], box2[0])\n",
    "    yi1 = max(box1[1], box2[1])\n",
    "    xi2 = min(box1[2], box2[2])\n",
    "    yi2 = min(box1[3], box2[3])\n",
    "    inter_area = (yi2-yi1) * (xi2-xi1)\n",
    "    ### END CODE HERE ###    \n",
    "\n",
    "    # Calculate the Union area by using Formula: Union(A,B) = A + B - Inter(A,B)\n",
    "    ### START CODE HERE ### (≈ 3 lines)\n",
    "    box1_area = (box1[2]-box1[0]) * (box1[3]-box1[1])\n",
    "    box2_area = (box2[2]-box2[0]) * (box2[3]-box2[1])\n",
    "    union_area = box1_area + box2_area - inter_area\n",
    "    ### END CODE HERE ###\n",
    "    \n",
    "    # compute the IoU\n",
    "    ### START CODE HERE ### (≈ 1 line)\n",
    "    iou = inter_area / union_area\n",
    "    ### END CODE HERE ###\n",
    "\n",
    "    return iou"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iou = 0.14285714285714285\n"
     ]
    }
   ],
   "source": [
    "box1 = (2, 1, 4, 3)\n",
    "box2 = (1, 2, 3, 4) \n",
    "print(\"iou = \" + str(iou(box1, box2)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Expected Output**:\n",
    "\n",
    "<table>\n",
    "    <tr>\n",
    "        <td>\n",
    "            **iou = **\n",
    "        </td>\n",
    "        <td>\n",
    "           0.14285714285714285\n",
    "        </td>\n",
    "    </tr>\n",
    "\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You are now ready to implement non-max suppression. The key steps are: \n",
    "1. Select the box that has the highest score.\n",
    "2. Compute its overlap with all other boxes, and remove boxes that overlap it more than `iou_threshold`.\n",
    "3. Go back to step 1 and iterate until there's no more boxes with a lower score than the current selected box.\n",
    "\n",
    "This will remove all boxes that have a large overlap with the selected boxes. Only the \"best\" boxes remain.\n",
    "\n",
    "**Exercise**: Implement yolo_non_max_suppression() using TensorFlow. TensorFlow has two built-in functions that are used to implement non-max suppression (so you don't actually need to use your `iou()` implementation):\n",
    "- [tf.image.non_max_suppression()](https://www.tensorflow.org/api_docs/python/tf/image/non_max_suppression)\n",
    "- [K.gather()](https://www.tensorflow.org/api_docs/python/tf/gather)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# GRADED FUNCTION: yolo_non_max_suppression\n",
    "\n",
    "def yolo_non_max_suppression(scores, boxes, classes, max_boxes = 10, iou_threshold = 0.5):\n",
    "    \"\"\"\n",
    "    Applies Non-max suppression (NMS) to set of boxes\n",
    "    \n",
    "    Arguments:\n",
    "    scores -- tensor of shape (None,), output of yolo_filter_boxes()\n",
    "    boxes -- tensor of shape (None, 4), output of yolo_filter_boxes() that have been scaled to the image size (see later)\n",
    "    classes -- tensor of shape (None,), output of yolo_filter_boxes()\n",
    "    max_boxes -- integer, maximum number of predicted boxes you'd like\n",
    "    iou_threshold -- real value, \"intersection over union\" threshold used for NMS filtering\n",
    "    \n",
    "    Returns:\n",
    "    scores -- tensor of shape (, None), predicted score for each box\n",
    "    boxes -- tensor of shape (4, None), predicted box coordinates\n",
    "    classes -- tensor of shape (, None), predicted class for each box\n",
    "    \n",
    "    Note: The \"None\" dimension of the output tensors has obviously to be less than max_boxes. Note also that this\n",
    "    function will transpose the shapes of scores, boxes, classes. This is made for convenience.\n",
    "    \"\"\"\n",
    "    \n",
    "    max_boxes_tensor = K.variable(max_boxes, dtype='int32') # tensor to be used in tf.image.non_max_suppression()\n",
    "    K.get_session().run(tf.variables_initializer([max_boxes_tensor])) # initialize variable max_boxes_tensor\n",
    "    # Use tf.image.non_max_suppression() to get the list of indices corresponding to boxes you keep\n",
    "    ### START CODE HERE ### (≈ 1 line)\n",
    "    #返回值是boxes中的偏移量\n",
    "    #非极大值抑制， 从这些向量中拿到score最大的几个，然后其他和这几个进行比较， iou>0.5的过滤掉\n",
    "    nms_indices = tf.image.non_max_suppression(boxes,scores, max_boxes_tensor, iou_threshold)\n",
    "    ### END CODE HERE ###\n",
    "    \n",
    "    # Use K.gather() to select only nms_indices from scores, boxes and classes\n",
    "    ### START CODE HERE ### (≈ 3 lines)\n",
    "    scores = tf.gather(scores, nms_indices) #gather 根据偏移量从scores中获取对应的值\n",
    "    boxes = tf.gather(boxes, nms_indices)\n",
    "    classes = tf.gather(classes, nms_indices)\n",
    "    ### END CODE HERE ###\n",
    "    \n",
    "    return scores, boxes, classes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<tf.Variable 'Variable_2:0' shape=() dtype=int32_ref>\n",
      "scores[2] = 6.938395\n",
      "boxes[2] = [-5.299932    3.1379814   4.450367    0.95942086]\n",
      "classes[2] = -2.2452729\n",
      "scores.shape = (10,)\n",
      "boxes.shape = (10, 4)\n",
      "classes.shape = (10,)\n"
     ]
    }
   ],
   "source": [
    "with tf.Session() as test_b:\n",
    "    scores = tf.random_normal([54,], mean=1, stddev=4, seed = 1)\n",
    "    boxes = tf.random_normal([54, 4], mean=1, stddev=4, seed = 1)\n",
    "    classes = tf.random_normal([54,], mean=1, stddev=4, seed = 1)\n",
    "    scores, boxes, classes = yolo_non_max_suppression(scores, boxes, classes)\n",
    "    print(\"scores[2] = \" + str(scores[2].eval()))\n",
    "    print(\"boxes[2] = \" + str(boxes[2].eval()))\n",
    "    print(\"classes[2] = \" + str(classes[2].eval()))\n",
    "    print(\"scores.shape = \" + str(scores.eval().shape))\n",
    "    print(\"boxes.shape = \" + str(boxes.eval().shape))\n",
    "    print(\"classes.shape = \" + str(classes.eval().shape))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Expected Output**:\n",
    "\n",
    "<table>\n",
    "    <tr>\n",
    "        <td>\n",
    "            **scores[2]**\n",
    "        </td>\n",
    "        <td>\n",
    "           6.9384\n",
    "        </td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "        <td>\n",
    "            **boxes[2]**\n",
    "        </td>\n",
    "        <td>\n",
    "           [-5.299932    3.13798141  4.45036697  0.95942086]\n",
    "        </td>\n",
    "    </tr>\n",
    "\n",
    "    <tr>\n",
    "        <td>\n",
    "            **classes[2]**\n",
    "        </td>\n",
    "        <td>\n",
    "           -2.24527\n",
    "        </td>\n",
    "    </tr>\n",
    "        <tr>\n",
    "        <td>\n",
    "            **scores.shape**\n",
    "        </td>\n",
    "        <td>\n",
    "           (10,)\n",
    "        </td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "        <td>\n",
    "            **boxes.shape**\n",
    "        </td>\n",
    "        <td>\n",
    "           (10, 4)\n",
    "        </td>\n",
    "    </tr>\n",
    "\n",
    "    <tr>\n",
    "        <td>\n",
    "            **classes.shape**\n",
    "        </td>\n",
    "        <td>\n",
    "           (10,)\n",
    "        </td>\n",
    "    </tr>\n",
    "\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.4 Wrapping up the filtering\n",
    "\n",
    "It's time to implement a function taking the output of the deep CNN (the 19x19x5x85 dimensional encoding) and filtering through all the boxes using the functions you've just implemented. \n",
    "\n",
    "**Exercise**: Implement `yolo_eval()` which takes the output of the YOLO encoding and filters the boxes using score threshold and NMS. There's just one last implementational detail you have to know. There're a few ways of representing boxes, such as via their corners or via their midpoint and height/width. YOLO converts between a few such formats at different times, using the following functions (which we have provided): \n",
    "\n",
    "```python\n",
    "boxes = yolo_boxes_to_corners(box_xy, box_wh) \n",
    "```\n",
    "which converts the yolo box coordinates (x,y,w,h) to box corners' coordinates (x1, y1, x2, y2) to fit the input of `yolo_filter_boxes`\n",
    "```python\n",
    "boxes = scale_boxes(boxes, image_shape)\n",
    "```\n",
    "YOLO's network was trained to run on 608x608 images. If you are testing this data on a different size image--for example, the car detection dataset had 720x1280 images--this step rescales the boxes so that they can be plotted on top of the original 720x1280 image.  \n",
    "\n",
    "Don't worry about these two functions; we'll show you where they need to be called.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# GRADED FUNCTION: yolo_eval\n",
    "\n",
    "def yolo_eval(yolo_outputs, image_shape = (720., 1280.), max_boxes=10, score_threshold=.6, iou_threshold=.5):\n",
    "    \"\"\"\n",
    "    Converts the output of YOLO encoding (a lot of boxes) to your predicted boxes along with their scores, box coordinates and classes.\n",
    "    \n",
    "    Arguments:\n",
    "    yolo_outputs -- output of the encoding model (for image_shape of (608, 608, 3)), contains 4 tensors:\n",
    "                    box_confidence: tensor of shape (None, 19, 19, 5, 1)\n",
    "                    box_xy: tensor of shape (None, 19, 19, 5, 2)\n",
    "                    box_wh: tensor of shape (None, 19, 19, 5, 2)\n",
    "                    box_class_probs: tensor of shape (None, 19, 19, 5, 80)\n",
    "    image_shape -- tensor of shape (2,) containing the input shape, in this notebook we use (608., 608.) (has to be float32 dtype)\n",
    "    max_boxes -- integer, maximum number of predicted boxes you'd like\n",
    "    score_threshold -- real value, if [ highest class probability score < threshold], then get rid of the corresponding box\n",
    "    iou_threshold -- real value, \"intersection over union\" threshold used for NMS filtering\n",
    "    \n",
    "    Returns:\n",
    "    scores -- tensor of shape (None, ), predicted score for each box\n",
    "    boxes -- tensor of shape (None, 4), predicted box coordinates\n",
    "    classes -- tensor of shape (None,), predicted class for each box\n",
    "    \"\"\"\n",
    "    \n",
    "    ### START CODE HERE ### \n",
    "    \n",
    "    # Retrieve outputs of the YOLO model (≈1 line)\n",
    "    box_confidence, box_xy, box_wh, box_class_probs = yolo_outputs\n",
    "\n",
    "    # Convert boxes to be ready for filtering functions \n",
    "    boxes = yolo_boxes_to_corners(box_xy, box_wh)\n",
    "\n",
    "    # Use one of the functions you've implemented to perform Score-filtering with a threshold of score_threshold (≈1 line)\n",
    "    scores, boxes, classes = yolo_filter_boxes(box_confidence, boxes, box_class_probs, score_threshold)\n",
    "    \n",
    "    # Scale boxes back to original image shape.\n",
    "    boxes = scale_boxes(boxes, image_shape)\n",
    "\n",
    "    # Use one of the functions you've implemented to perform Non-max suppression with a threshold of iou_threshold (≈1 line)\n",
    "    scores, boxes, classes = yolo_non_max_suppression(scores, boxes, classes, max_boxes, iou_threshold)\n",
    "    \n",
    "    ### END CODE HERE ###\n",
    "    \n",
    "    return scores, boxes, classes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<tf.Variable 'Variable_3:0' shape=() dtype=int32_ref>\n",
      "scores[2] = 138.79124\n",
      "boxes[2] = [1292.3297  -278.52167 3876.9893  -835.56494]\n",
      "classes[2] = 54\n",
      "scores.shape = (10,)\n",
      "boxes.shape = (10, 4)\n",
      "classes.shape = (10,)\n"
     ]
    }
   ],
   "source": [
    "with tf.Session() as test_b:\n",
    "    yolo_outputs = (tf.random_normal([19, 19, 5, 1], mean=1, stddev=4, seed = 1),\n",
    "                    tf.random_normal([19, 19, 5, 2], mean=1, stddev=4, seed = 1),\n",
    "                    tf.random_normal([19, 19, 5, 2], mean=1, stddev=4, seed = 1),\n",
    "                    tf.random_normal([19, 19, 5, 80], mean=1, stddev=4, seed = 1))\n",
    "    scores, boxes, classes = yolo_eval(yolo_outputs)\n",
    "    print(\"scores[2] = \" + str(scores[2].eval()))\n",
    "    print(\"boxes[2] = \" + str(boxes[2].eval()))\n",
    "    print(\"classes[2] = \" + str(classes[2].eval()))\n",
    "    print(\"scores.shape = \" + str(scores.eval().shape))\n",
    "    print(\"boxes.shape = \" + str(boxes.eval().shape))\n",
    "    print(\"classes.shape = \" + str(classes.eval().shape))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "**Expected Output**:\n",
    "\n",
    "<table>\n",
    "    <tr>\n",
    "        <td>\n",
    "            **scores[2]**\n",
    "        </td>\n",
    "        <td>\n",
    "           138.791\n",
    "        </td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "        <td>\n",
    "            **boxes[2]**\n",
    "        </td>\n",
    "        <td>\n",
    "           [ 1292.32971191  -278.52166748  3876.98925781  -835.56494141]\n",
    "        </td>\n",
    "    </tr>\n",
    "\n",
    "    <tr>\n",
    "        <td>\n",
    "            **classes[2]**\n",
    "        </td>\n",
    "        <td>\n",
    "           54\n",
    "        </td>\n",
    "    </tr>\n",
    "        <tr>\n",
    "        <td>\n",
    "            **scores.shape**\n",
    "        </td>\n",
    "        <td>\n",
    "           (10,)\n",
    "        </td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "        <td>\n",
    "            **boxes.shape**\n",
    "        </td>\n",
    "        <td>\n",
    "           (10, 4)\n",
    "        </td>\n",
    "    </tr>\n",
    "\n",
    "    <tr>\n",
    "        <td>\n",
    "            **classes.shape**\n",
    "        </td>\n",
    "        <td>\n",
    "           (10,)\n",
    "        </td>\n",
    "    </tr>\n",
    "\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color='blue'>\n",
    "**Summary for YOLO**:\n",
    "- Input image (608, 608, 3)\n",
    "- The input image goes through a CNN, resulting in a (19,19,5,85) dimensional output. \n",
    "- After flattening the last two dimensions, the output is a volume of shape (19, 19, 425):\n",
    "    - Each cell in a 19x19 grid over the input image gives 425 numbers. \n",
    "    - 425 = 5 x 85 because each cell contains predictions for 5 boxes, corresponding to 5 anchor boxes, as seen in lecture. \n",
    "    - 85 = 5 + 80 where 5 is because $(p_c, b_x, b_y, b_h, b_w)$ has 5 numbers, and and 80 is the number of classes we'd like to detect\n",
    "- You then select only few boxes based on:\n",
    "    - Score-thresholding: throw away boxes that have detected a class with a score less than the threshold\n",
    "    - Non-max suppression: Compute the Intersection over Union and avoid selecting overlapping boxes\n",
    "- This gives you YOLO's final output. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3 - Test YOLO pretrained model on images"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this part, you are going to use a pretrained model and test it on the car detection dataset. As usual, you start by **creating a session to start your graph**. Run the following cell."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "sess = K.get_session()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.1 - Defining classes, anchors and image shape."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Recall that we are trying to detect 80 classes, and are using 5 anchor boxes. We have gathered the information about the 80 classes and 5 boxes in two files \"coco_classes.txt\" and \"yolo_anchors.txt\". Let's load these quantities into the model by running the next cell. \n",
    "\n",
    "The car detection dataset has 720x1280 images, which we've pre-processed into 608x608 images. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "class_names = read_classes(\"model_data/coco_classes.txt\")\n",
    "anchors = read_anchors(\"model_data/yolo_anchors.txt\")\n",
    "image_shape = (720., 1280.)    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.2 - Loading a pretrained model\n",
    "\n",
    "Training a YOLO model takes a very long time and requires a fairly large dataset of labelled bounding boxes for a large range of target classes. You are going to load an existing pretrained Keras YOLO model stored in \"yolo.h5\". (These weights come from the official YOLO website, and were converted using a function written by Allan Zelener. References are at the end of this notebook. Technically, these are the parameters from the \"YOLOv2\" model, but we will more simply refer to it as \"YOLO\" in this notebook.) Run the cell below to load the model from this file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\program_files\\anaconda\\lib\\site-packages\\keras\\models.py:282: UserWarning: No training configuration found in save file: the model was *not* compiled. Compile it manually.\n",
      "  warnings.warn('No training configuration found in save file: '\n"
     ]
    }
   ],
   "source": [
    "yolo_model = load_model(\"model_data/yolo.h5\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This loads the weights of a trained YOLO model. Here's a summary of the layers your model contains."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input_1 (InputLayer)            (None, 608, 608, 3)  0                                            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_1 (Conv2D)               (None, 608, 608, 32) 864         input_1[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_1 (BatchNor (None, 608, 608, 32) 128         conv2d_1[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "leaky_re_lu_1 (LeakyReLU)       (None, 608, 608, 32) 0           batch_normalization_1[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_1 (MaxPooling2D)  (None, 304, 304, 32) 0           leaky_re_lu_1[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_2 (Conv2D)               (None, 304, 304, 64) 18432       max_pooling2d_1[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_2 (BatchNor (None, 304, 304, 64) 256         conv2d_2[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "leaky_re_lu_2 (LeakyReLU)       (None, 304, 304, 64) 0           batch_normalization_2[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_2 (MaxPooling2D)  (None, 152, 152, 64) 0           leaky_re_lu_2[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_3 (Conv2D)               (None, 152, 152, 128 73728       max_pooling2d_2[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_3 (BatchNor (None, 152, 152, 128 512         conv2d_3[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "leaky_re_lu_3 (LeakyReLU)       (None, 152, 152, 128 0           batch_normalization_3[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_4 (Conv2D)               (None, 152, 152, 64) 8192        leaky_re_lu_3[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_4 (BatchNor (None, 152, 152, 64) 256         conv2d_4[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "leaky_re_lu_4 (LeakyReLU)       (None, 152, 152, 64) 0           batch_normalization_4[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_5 (Conv2D)               (None, 152, 152, 128 73728       leaky_re_lu_4[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_5 (BatchNor (None, 152, 152, 128 512         conv2d_5[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "leaky_re_lu_5 (LeakyReLU)       (None, 152, 152, 128 0           batch_normalization_5[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_3 (MaxPooling2D)  (None, 76, 76, 128)  0           leaky_re_lu_5[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_6 (Conv2D)               (None, 76, 76, 256)  294912      max_pooling2d_3[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_6 (BatchNor (None, 76, 76, 256)  1024        conv2d_6[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "leaky_re_lu_6 (LeakyReLU)       (None, 76, 76, 256)  0           batch_normalization_6[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_7 (Conv2D)               (None, 76, 76, 128)  32768       leaky_re_lu_6[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_7 (BatchNor (None, 76, 76, 128)  512         conv2d_7[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "leaky_re_lu_7 (LeakyReLU)       (None, 76, 76, 128)  0           batch_normalization_7[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_8 (Conv2D)               (None, 76, 76, 256)  294912      leaky_re_lu_7[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_8 (BatchNor (None, 76, 76, 256)  1024        conv2d_8[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "leaky_re_lu_8 (LeakyReLU)       (None, 76, 76, 256)  0           batch_normalization_8[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_4 (MaxPooling2D)  (None, 38, 38, 256)  0           leaky_re_lu_8[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_9 (Conv2D)               (None, 38, 38, 512)  1179648     max_pooling2d_4[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_9 (BatchNor (None, 38, 38, 512)  2048        conv2d_9[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "leaky_re_lu_9 (LeakyReLU)       (None, 38, 38, 512)  0           batch_normalization_9[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_10 (Conv2D)              (None, 38, 38, 256)  131072      leaky_re_lu_9[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_10 (BatchNo (None, 38, 38, 256)  1024        conv2d_10[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "leaky_re_lu_10 (LeakyReLU)      (None, 38, 38, 256)  0           batch_normalization_10[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_11 (Conv2D)              (None, 38, 38, 512)  1179648     leaky_re_lu_10[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_11 (BatchNo (None, 38, 38, 512)  2048        conv2d_11[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "leaky_re_lu_11 (LeakyReLU)      (None, 38, 38, 512)  0           batch_normalization_11[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_12 (Conv2D)              (None, 38, 38, 256)  131072      leaky_re_lu_11[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_12 (BatchNo (None, 38, 38, 256)  1024        conv2d_12[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "leaky_re_lu_12 (LeakyReLU)      (None, 38, 38, 256)  0           batch_normalization_12[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_13 (Conv2D)              (None, 38, 38, 512)  1179648     leaky_re_lu_12[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_13 (BatchNo (None, 38, 38, 512)  2048        conv2d_13[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "leaky_re_lu_13 (LeakyReLU)      (None, 38, 38, 512)  0           batch_normalization_13[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_5 (MaxPooling2D)  (None, 19, 19, 512)  0           leaky_re_lu_13[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_14 (Conv2D)              (None, 19, 19, 1024) 4718592     max_pooling2d_5[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_14 (BatchNo (None, 19, 19, 1024) 4096        conv2d_14[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "leaky_re_lu_14 (LeakyReLU)      (None, 19, 19, 1024) 0           batch_normalization_14[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_15 (Conv2D)              (None, 19, 19, 512)  524288      leaky_re_lu_14[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_15 (BatchNo (None, 19, 19, 512)  2048        conv2d_15[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "leaky_re_lu_15 (LeakyReLU)      (None, 19, 19, 512)  0           batch_normalization_15[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_16 (Conv2D)              (None, 19, 19, 1024) 4718592     leaky_re_lu_15[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_16 (BatchNo (None, 19, 19, 1024) 4096        conv2d_16[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "leaky_re_lu_16 (LeakyReLU)      (None, 19, 19, 1024) 0           batch_normalization_16[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_17 (Conv2D)              (None, 19, 19, 512)  524288      leaky_re_lu_16[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_17 (BatchNo (None, 19, 19, 512)  2048        conv2d_17[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "leaky_re_lu_17 (LeakyReLU)      (None, 19, 19, 512)  0           batch_normalization_17[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_18 (Conv2D)              (None, 19, 19, 1024) 4718592     leaky_re_lu_17[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_18 (BatchNo (None, 19, 19, 1024) 4096        conv2d_18[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "leaky_re_lu_18 (LeakyReLU)      (None, 19, 19, 1024) 0           batch_normalization_18[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_19 (Conv2D)              (None, 19, 19, 1024) 9437184     leaky_re_lu_18[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_19 (BatchNo (None, 19, 19, 1024) 4096        conv2d_19[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_21 (Conv2D)              (None, 38, 38, 64)   32768       leaky_re_lu_13[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "leaky_re_lu_19 (LeakyReLU)      (None, 19, 19, 1024) 0           batch_normalization_19[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_21 (BatchNo (None, 38, 38, 64)   256         conv2d_21[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_20 (Conv2D)              (None, 19, 19, 1024) 9437184     leaky_re_lu_19[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "leaky_re_lu_21 (LeakyReLU)      (None, 38, 38, 64)   0           batch_normalization_21[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_20 (BatchNo (None, 19, 19, 1024) 4096        conv2d_20[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "space_to_depth_x2 (Lambda)      (None, 19, 19, 256)  0           leaky_re_lu_21[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "leaky_re_lu_20 (LeakyReLU)      (None, 19, 19, 1024) 0           batch_normalization_20[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_1 (Concatenate)     (None, 19, 19, 1280) 0           space_to_depth_x2[0][0]          \n",
      "                                                                 leaky_re_lu_20[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_22 (Conv2D)              (None, 19, 19, 1024) 11796480    concatenate_1[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_22 (BatchNo (None, 19, 19, 1024) 4096        conv2d_22[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "leaky_re_lu_22 (LeakyReLU)      (None, 19, 19, 1024) 0           batch_normalization_22[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_23 (Conv2D)              (None, 19, 19, 425)  435625      leaky_re_lu_22[0][0]             \n",
      "==================================================================================================\n",
      "Total params: 50,983,561\n",
      "Trainable params: 50,962,889\n",
      "Non-trainable params: 20,672\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "yolo_model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note**: On some computers, you may see a warning message from Keras. Don't worry about it if you do--it is fine.\n",
    "\n",
    "**Reminder**: this model converts a preprocessed batch of input images (shape: (m, 608, 608, 3)) into a tensor of shape (m, 19, 19, 5, 85) as explained in Figure (2)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.3 - Convert output of the model to usable bounding box tensors\n",
    "\n",
    "The output of `yolo_model` is a (m, 19, 19, 5, 85) tensor that needs to pass through non-trivial processing and conversion. The following cell does that for you."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "yolo_outputs = yolo_head(yolo_model.output, anchors, len(class_names))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You added `yolo_outputs` to your graph. This set of 4 tensors is ready to be used as input by your `yolo_eval` function."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.4 - Filtering boxes\n",
    "\n",
    "`yolo_outputs` gave you all the predicted boxes of `yolo_model` in the correct format. You're now ready to perform filtering and select only the best boxes. Lets now call `yolo_eval`, which you had previously implemented, to do this. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<tf.Variable 'Variable_6:0' shape=() dtype=int32_ref>\n"
     ]
    }
   ],
   "source": [
    "scores, boxes, classes = yolo_eval(yolo_outputs, image_shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.5 - Run the graph on an image\n",
    "\n",
    "Let the fun begin. You have created a (`sess`) graph that can be summarized as follows:\n",
    "\n",
    "1. <font color='purple'> yolo_model.input </font> is given to `yolo_model`. The model is used to compute the output <font color='purple'> yolo_model.output </font>\n",
    "2. <font color='purple'> yolo_model.output </font> is processed by `yolo_head`. It gives you <font color='purple'> yolo_outputs </font>\n",
    "3. <font color='purple'> yolo_outputs </font> goes through a filtering function, `yolo_eval`. It outputs your predictions: <font color='purple'> scores, boxes, classes </font>\n",
    "\n",
    "**Exercise**: Implement predict() which runs the graph to test YOLO on an image.\n",
    "You will need to run a TensorFlow session, to have it compute `scores, boxes, classes`.\n",
    "\n",
    "The code below also uses the following function:\n",
    "```python\n",
    "image, image_data = preprocess_image(\"images/\" + image_file, model_image_size = (608, 608))\n",
    "```\n",
    "which outputs:\n",
    "- image: a python (PIL) representation of your image used for drawing boxes. You won't need to use it.\n",
    "- image_data: a numpy-array representing the image. This will be the input to the CNN.\n",
    "\n",
    "**Important note**: when a model uses BatchNorm (as is the case in YOLO), you will need to pass an additional placeholder in the feed_dict {K.learning_phase(): 0}."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "def predict(sess, image_file):\n",
    "    \"\"\"\n",
    "    Runs the graph stored in \"sess\" to predict boxes for \"image_file\". Prints and plots the preditions.\n",
    "    \n",
    "    Arguments:\n",
    "    sess -- your tensorflow/Keras session containing the YOLO graph\n",
    "    image_file -- name of an image stored in the \"images\" folder.\n",
    "    \n",
    "    Returns:\n",
    "    out_scores -- tensor of shape (None, ), scores of the predicted boxes\n",
    "    out_boxes -- tensor of shape (None, 4), coordinates of the predicted boxes\n",
    "    out_classes -- tensor of shape (None, ), class index of the predicted boxes\n",
    "    \n",
    "    Note: \"None\" actually represents the number of predicted boxes, it varies between 0 and max_boxes. \n",
    "    \"\"\"\n",
    "\n",
    "    # Preprocess your image\n",
    "    image, image_data = preprocess_image(\"images/\" + image_file, model_image_size = (608, 608))\n",
    "\n",
    "    # Run the session with the correct tensors and choose the correct placeholders in the feed_dict.\n",
    "    # You'll need to use feed_dict={yolo_model.input: ... , K.learning_phase(): 0})\n",
    "    ### START CODE HERE ### (≈ 1 line)\n",
    "    out_scores, out_boxes, out_classes = sess.run([scores, boxes, classes], \n",
    "                                                  feed_dict={yolo_model.input:image_data, K.learning_phase(): 0})\n",
    "    ### END CODE HERE ###\n",
    "\n",
    "    # Print predictions info\n",
    "    print('Found {} boxes for {}'.format(len(out_boxes), image_file))\n",
    "    # Generate colors for drawing bounding boxes.\n",
    "    colors = generate_colors(class_names)\n",
    "    # Draw bounding boxes on the image file\n",
    "    draw_boxes(image, out_scores, out_boxes, out_classes, class_names, colors)\n",
    "    # Save the predicted bounding box on the image\n",
    "    image.save(os.path.join(\"out\", image_file), quality=90)\n",
    "    # Display the results in the notebook\n",
    "    output_image = scipy.misc.imread(os.path.join(\"out\", image_file))\n",
    "    imshow(output_image)\n",
    "    \n",
    "    return out_scores, out_boxes, out_classes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Run the following cell on the \"test.jpg\" image to verify that your function is correct."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 7 boxes for test.jpg\n",
      "car 0.60 (925, 285) (1045, 374)\n",
      "car 0.66 (706, 279) (786, 350)\n",
      "bus 0.67 (5, 266) (220, 407)\n",
      "car 0.70 (947, 324) (1280, 705)\n",
      "car 0.74 (159, 303) (346, 440)\n",
      "car 0.80 (761, 282) (942, 412)\n",
      "car 0.89 (367, 300) (745, 648)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\program_files\\anaconda\\lib\\site-packages\\ipykernel_launcher.py:36: DeprecationWarning: `imread` is deprecated!\n",
      "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n",
      "Use ``imageio.imread`` instead.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1d17096f400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "out_scores, out_boxes, out_classes = predict(sess, \"test.jpg\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Expected Output**:\n",
    "\n",
    "<table>\n",
    "    <tr>\n",
    "        <td>\n",
    "            **Found 7 boxes for test.jpg**\n",
    "        </td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "        <td>\n",
    "            **car**\n",
    "        </td>\n",
    "        <td>\n",
    "           0.60 (925, 285) (1045, 374)\n",
    "        </td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "        <td>\n",
    "            **car**\n",
    "        </td>\n",
    "        <td>\n",
    "           0.66 (706, 279) (786, 350)\n",
    "        </td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "        <td>\n",
    "            **bus**\n",
    "        </td>\n",
    "        <td>\n",
    "           0.67 (5, 266) (220, 407)\n",
    "        </td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "        <td>\n",
    "            **car**\n",
    "        </td>\n",
    "        <td>\n",
    "           0.70 (947, 324) (1280, 705)\n",
    "        </td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "        <td>\n",
    "            **car**\n",
    "        </td>\n",
    "        <td>\n",
    "           0.74 (159, 303) (346, 440)\n",
    "        </td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "        <td>\n",
    "            **car**\n",
    "        </td>\n",
    "        <td>\n",
    "           0.80 (761, 282) (942, 412)\n",
    "        </td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "        <td>\n",
    "            **car**\n",
    "        </td>\n",
    "        <td>\n",
    "           0.89 (367, 300) (745, 648)\n",
    "        </td>\n",
    "    </tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The model you've just run is actually able to detect 80 different classes listed in \"coco_classes.txt\". To test the model on your own images:\n",
    "    1. Click on \"File\" in the upper bar of this notebook, then click \"Open\" to go on your Coursera Hub.\n",
    "    2. Add your image to this Jupyter Notebook's directory, in the \"images\" folder\n",
    "    3. Write your image's name in the cell above code\n",
    "    4. Run the code and see the output of the algorithm!\n",
    "\n",
    "If you were to run your session in a for loop over all your images. Here's what you would get:\n",
    "\n",
    "<center>\n",
    "<video width=\"400\" height=\"200\" src=\"nb_images/pred_video_compressed2.mp4\" type=\"video/mp4\" controls>\n",
    "</video>\n",
    "</center>\n",
    "\n",
    "<caption><center> Predictions of the YOLO model on pictures taken from a camera while driving around the Silicon Valley <br> Thanks [drive.ai](https://www.drive.ai/) for providing this dataset! </center></caption>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "<font color='blue'>\n",
    "**What you should remember**:\n",
    "- YOLO is a state-of-the-art object detection model that is fast and accurate\n",
    "- It runs an input image through a CNN which outputs a 19x19x5x85 dimensional volume. \n",
    "- The encoding can be seen as a grid where each of the 19x19 cells contains information about 5 boxes.\n",
    "- You filter through all the boxes using non-max suppression. Specifically: \n",
    "    - Score thresholding on the probability of detecting a class to keep only accurate (high probability) boxes\n",
    "    - Intersection over Union (IoU) thresholding to eliminate overlapping boxes\n",
    "- Because training a YOLO model from randomly initialized weights is non-trivial and requires a large dataset as well as lot of computation, we used previously trained model parameters in this exercise. If you wish, you can also try fine-tuning the YOLO model with your own dataset, though this would be a fairly non-trivial exercise. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**References**: The ideas presented in this notebook came primarily from the two YOLO papers. The implementation here also took significant inspiration and used many components from Allan Zelener's github repository. The pretrained weights used in this exercise came from the official YOLO website. \n",
    "- Joseph Redmon, Santosh Divvala, Ross Girshick, Ali Farhadi - [You Only Look Once: Unified, Real-Time Object Detection](https://arxiv.org/abs/1506.02640) (2015)\n",
    "- Joseph Redmon, Ali Farhadi - [YOLO9000: Better, Faster, Stronger](https://arxiv.org/abs/1612.08242) (2016)\n",
    "- Allan Zelener - [YAD2K: Yet Another Darknet 2 Keras](https://github.com/allanzelener/YAD2K)\n",
    "- The official YOLO website (https://pjreddie.com/darknet/yolo/) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Car detection dataset**:\n",
    "<a rel=\"license\" href=\"http://creativecommons.org/licenses/by/4.0/\"><img alt=\"Creative Commons License\" style=\"border-width:0\" src=\"https://i.creativecommons.org/l/by/4.0/88x31.png\" /></a><br /><span xmlns:dct=\"http://purl.org/dc/terms/\" property=\"dct:title\">The Drive.ai Sample Dataset</span> (provided by drive.ai) is licensed under a <a rel=\"license\" href=\"http://creativecommons.org/licenses/by/4.0/\">Creative Commons Attribution 4.0 International License</a>. We are especially grateful to Brody Huval, Chih Hu and Rahul Patel for collecting and providing this dataset. "
   ]
  }
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